Comparative analysis of epidemic characteristics and prevention and control measures of corona virus disease 2019 in Russian Federation

Objective: This article analyzes the epidemic situation and characteristics of Corona virus disease 2019 (COVID-19) in Russian Federation (referred to as Russia). summarizes the effective measures and problems exposed by Russia to deal with COVID-19, so as to provide reference for our country's epidemic prevention and control, and seek the direction of cooperation under the background of Sino Russia scientific and technological innovation in view of public health emergency.

Comparative analysis of epidemic characteristics and prevention and control measures of corona virus disease 2019 in South Korea and Japan

Objective: By analyzing epidemic characteristics and prevention and control measures of Corona virus disease 2019 (COVID-19) in South Korea and Japan, to provide reference for prevention and control of the epidemic.

Mortality in Four Waves of COVID-19 Is Differently Associated with Healthcare Capacities Affected by Economic Disparities.

BACKGROUND: The greatest challenges are imposed on the overall capacity of disease management when the cases reach the maximum in each wave of the pandemic. METHODS: The cases and deaths for the four waves of COVID-19 in 119 countries and regions (CRs) were collected. We compared the mortality across CRs where populations experience different economic and healthcare disparities. FINDINGS: Among 119 CRs, 117, 112, 111, and 55 have experienced 1, 2, 3, and 4 waves of COVID-19 disease, respectively. The average mortality rates at the disease turning point were 0.036, 0.019. 0.017, and 0.015 for the waves 1, 2, 3, and 4, respectively. Among 49 potential factors, income level, gross national income (GNI) per capita, and school enrollment are positively correlated with the mortality rates in the first wave, but negatively correlated with the rates of the rest of the waves. Their values for the first wave are 0.253, 0.346 and 0.385, respectively. The r value for waves 2, 3, and 4 are -0.310, -0.293, -0.234; -0.263, -0.284, -0.282; and -0.330, -0.394, -0.048, respectively. In high-income CRs, the mortality rates in waves 2 and 3 were 29% and 28% of that in wave 1; while in upper-middle-income CRs, the rates for waves 2 and 3 were 76% and 79% of that in wave 1. The rates in waves 2 and 3 for lower-middle-income countries were 88% and 89% of that in wave 1, and for low-income countries were 135% and 135%. Furthermore, comparison among the largest case numbers through all waves indicated that the mortalities in upper- and lower-middle-income countries is 65% more than that of the high-income countries. INTERPRETATION: Conclusions from the first wave of the COVID-19 pandemic do not apply to the following waves. The clinical outcomes in developing countries become worse along with the expansion of the pandemic.

Variations of COVID-19 mortality are affected by economic disparities across countries.

BACKGROUND: When the COVID-19 case number reaches a maximum in a country, its capacity and management of health system face greatest challenge. METHODS: We performed a cross-sectional study on data of turning points for cases and deaths for the first three waves of COVID-19 in countries with more than 5000 cumulative cases, as reported by Worldometers and WHO Coronavirus (COVID-19) Dashboard. We compared the case fatality rates (CFRs) and time lags (in unit of day) between the turning points of cases and deaths among countries in different development stages and potential influence factors. As of May 10, 2021, 106 out of 222 countries or regions (56%) reported more than 5000 cases. Approximately half of them have experienced all the three waves of COVID-19 disease. The average mortality rate at the disease turning point was 0.038 for the first wave, 0.020 for the second wave, and 0.023 for wave 3. In high-income countries, the mortality rates during the first wave are higher than that of the other income levels. However, the mortality rates during the second and third waves of COVID-19 were much lower than those of the first wave, with a significant reduction from 5.7% to 1.7% approximately 70%. At the same time, high-income countries exhibited a 2-fold increase in time lags during the second and the third waves compared to the first wave, suggesting that the periods between the cases and deaths turning point extended. High rates in the first wave in developed countries are associated to multiple factors including transportation, population density, and aging populations. In upper middle- and lower middle-income countries, the decreasing of mortality rates in the second and third waves were subtle or even reversed, with increased mortality during the following waves. In the upper and lower middle-income countries, the time lags were about 50% of the durations observed from high-income countries. INTERPRETATION: Economy and medical resources affect the efficiency of COVID-19 mitigation and the clinical outcomes of the patients. The situation is likely to become even worse in the light of these countries' limited ability to combat COVID-19 and prevent severe outcomes or deaths as the new variant transmission becomes dominant.

Policy recommendations on strengthening the core of the Centers for Disease Control and Prevention in the response to major epidemics

The Centers for Disease Control and Prevention (CDC), is an elite force for disease prevention and control, serving as the core force for blocking and contain the epidemic. Which plays a central role in fighting COVID-19 epidemic in China. However, during the process, its also exposed some problems: lack of comprehensive capacity building program, fragmented knowledge and skills, epidemiological investigation instrument is outdated, insufficient training on emergency management ability, emphasize investment in infrastructure, equipment and techniques but pay less attention to constantly updating the risk monitoring and alerting system as well as other important coordinating mechanisms, which will affect the well functioning of CDC system. In order to effectively curb the possible rebound of this epidemic and prevent the recurrence of new infectious diseases, we urgently need to reflect and summarize the experience and lessons of this outbreak response, and put forward more targeted policy options for future improvement.

The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19.

The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis. A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates. Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA.

COVID-19 vaccine: Call for employees in international transportation industries and international travelers as the first priority in global distribution.

While countries are in a hurry to obtain SARS-CoV-2 vaccine, we are concerned with the availability of vaccine and whether a vaccine will be available to all in need. We predicted three possible scenarios for vaccine distributions and urge an international united action on the worldwide equitable access. In case the international community does not reach a consensus on how to distribute the vaccine to achieve worldwide equitable access, we call for a distribution plan that includes the employees in international transportation industries and international travelers to halt the disease transmission and promote the recovery of the global economy.

A cost-effective plan for global testing - an infection rate stratified, algorithm guided, multiple-level, continuously pooled testing strategy.

The most effective measure to prevent or stop the spread of infectious diseases is the early identification and isolation of infected individuals through comprehensive screening. At present, in the COVID-19 pandemic, such screening is often limited to isolated regions as determined by local governments. Screening of potentially infectious individuals should be conducted through coordinated national or global unified actions. Our current research focuses on using resources to conduct comprehensive national and regional regular testing with a risk rate based, algorithmic guided, multiple-level, pooled testing strategy. Here, combining methodologies with mathematical logistic models, we present an analytic procedure of an overall plan for coordinating state, national, or global testing. The proposed plan includes three parts 1) organization, resource allocation, and distribution; 2) screening based on different risk levels and business types; and 3) algorithm guided, multiple level, continuously screening the entire population in a region. This strategy will overcome the false positive and negative results in the polymerase chain reaction (PCR) test and missing samples during initial tests. Based on our proposed protocol, the population screening of 300,000,000 in the US can be done weekly with between 15,000,000 and 6,000,000 test kits. The strategy can be used for population screening for current COVID-19 and any future severe infectious disease when drugs or vaccines are not available.

Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm.

The global COVID-19 outbreak is worrisome both for its high rate of spread, and the high case fatality rate reported by early studies and now in Italy. We report a new methodology, the Patient Information Based Algorithm (PIBA), for estimating the death rate of a disease in real-time using publicly available data collected during an outbreak. PIBA estimated the death rate based on data of the patients in Wuhan and then in other cities throughout China. The estimated days from hospital admission to death was 13 (standard deviation (SD), 6 days). The death rates based on PIBA were used to predict the daily numbers of deaths since the week of February 25, 2020, in China overall, Hubei province, Wuhan city, and the rest of the country except Hubei province. The death rate of COVID-19 ranges from 0.75% to 3% and may decrease in the future. The results showed that the real death numbers had fallen into the predicted ranges. In addition, using the preliminary data from China, the PIBA method was successfully used to estimate the death rate and predict the death numbers of the Korean population. In conclusion, PIBA can be used to efficiently estimate the death rate of a new infectious disease in real-time and to predict future deaths. The spread of 2019-nCoV and its case fatality rate may vary in regions with different climates and temperatures from Hubei and Wuhan. PIBA model can be built based on known information of early patients in different countries.